Abstract

Numerous self-organizing systems can be found in nature that autonomously adapt to shifting circumstances without impairing the system's objectives. In order to conduct an energy-effective region sampling, we suggest a selforganizing sensor network that is modelled after actual systems. Using local data processing, mobile nodes in our network carry out certain rules. These principles give the nodes the ability to split the sampling duty so that they can self-organize to use less power overall and sample phenomena more accurately. The digital hormone-based model,which contains these regulations, offers a theoretical framework for analysing this group of systems. On cricket mote simulations, this model has been put into practise. Compared to a traditional model with fixed rate sampling, our findings show that the model is more efficient.

In transportation optimization, personnel scheduling, network routing, and other areas, the constrained shortest path (CSP) problem is frequently employed. As an NP-hard problem, it is still a matter of debate. The adaptive amoeba algorithm's fundamental mechanism is the foundation of the novel approach we provide in this paper. Two sections make up the suggested procedure. To resolve the shortest path problem in directed networks in the first section, we use the original amoeba approach. The Physarum algorithm and a rule with bio-inspired design.